李楷

个人信息Personal Information

副教授

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:船舶工程学院

学科:船舶与海洋结构物设计制造

电子邮箱:likai@dlut.edu.cn

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Hull form design optimization of twin-skeg fishing vessel for minimum resistance based on surrogate model

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论文类型:期刊论文

发表时间:2018-09-01

发表刊物:ADVANCES IN ENGINEERING SOFTWARE

收录刊物:SCIE

卷号:123

页面范围:38-50

ISSN号:0965-9978

关键字:Twin-skeg; Resistance optimization; CFD; Kriging modeling method; Multi-objective optimization

摘要:Twin-skeg ship has better hydrodynamic performances than regular ship, however, it is still difficult to obtain an accurate relationship between skeg design and overall hydrodynamic performances. Resistance optimization is the major concern of developing twin-skeg ship. This paper proposes a combined approach for hull form design optimization of twin-skeg ship by using computational fluid dynamics (CFD) calculation and surrogate model. Main design parameters of skeg geometry and arrangement could be determined from the design domain by using the proposed method. Parametric modeling technology is adopted for performing design evaluations in an automatic manner with different design parameter combinations. A twin-skeg fishing vessel is selected as research object. In the proposed method, the sample set for constructing surrogate models is generated by using Optimal Latin Hypercube Sampling (OLHS) method, the corresponding responses are calculated through CFD simulations, and then the surrogate models are constructed by using Kriging modeling method, which represent the mathematical relationship between input design variables (skeg shape design variables) and output objective functions (resistance values under four different working conditions). The functional analysis of variance (ANOVA) is performed to investigate how much influence the design variables have on the objective functions. Finally, a multi-objective evolutionary algorithm (NSGA-II) is used to obtain the optimal solution, which shows 5.4% average decrease in the total resistance than the original design. The CFD calculation results of the optimal solution show that the proposed method can achieve minimum resistance design with high accuracy and low time cost.